Apache Kafka is an open-source event streaming platform used for publishing and processing events at high throughput. It is designed to handle large volumes of data in real-time, making it a popular choice for building real-time data pipelines and streaming applications.
Kafka is a distributed system that allows applications to publish, subscribe to, store, and process streams of records. It achieves this through a high-performance TCP network protocol, ensuring fault-tolerant, distributed storage, and enabling real-time data processing.
To understand Kafka, it's important to be familiar with a few key concepts:
- Topics: A category or feed name to which records are published. Topics are partitioned and replicated across multiple servers.
- Producers: Applications that publish records to Kafka topics.
- Consumers: Applications that subscribe to Kafka topics and process the records.
- Brokers: Kafka servers that store and serve records to consumers.
- Partitions: A topic is divided into partitions, which are the basic unit of parallelism in Kafka.
Kafka provides several benefits that make it an ideal choice for real-time data streaming and processing:
- High Throughput: Kafka can handle millions of records per second, making it suitable for high-volume data streams.
- Scalability: Kafka's distributed architecture allows it to scale horizontally by adding more brokers to the cluster.
- Durability: Kafka replicates data across multiple brokers, ensuring that data is not lost even if a broker fails.
- Fault Tolerance: Kafka's design ensures that the system can continue to operate even in the presence of failures.
Kafka is widely used across various industries and for different use cases, including:
- Real-time Analytics: Processing and analyzing data streams in real-time.
- Event Sourcing: Capturing changes to application state as a sequence of events.
- Log Aggregation: Collecting and aggregating log data from multiple sources.
- Stream Processing: Building applications that transform or react to data streams.